R Notebook

What tweets got the most attention. frequency of tweets posted on a topic

twitteR::userTimeline("News24", n = 100)

Error in get_oauth_sig() : OAuth has not been registered for this session

Comparing Tweet Archives

ggplot(media_agency_df, aes(x = created_at, fill = screen_name)) +
  geom_histogram(position = "identity", bins = 20, show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 1)

#tidy df and unnest
tidy_covid_media_df <- media_agency_df %>% 
  #filter(str_detect(text, fixed(covid_dictionary, ignore_case = TRUE))) %>%
  filter(!str_detect(text, "^RT")) %>%
  mutate(text = gsub(" ?(f|ht)tp(s?)://(.*)[.][a-z]+", "", text)) %>%
  unnest_tokens(word, text, token = "tweets") %>% 
  filter(!word %in% stop_words$word,
         !word %in% str_remove_all(stop_words$word, "'"),
         str_detect(word, "[a-z]"),
         !(word =="sabcnews" |
             word =="enca" |
             word =="https" |
             word =="dstv" |
             word =="sabckzn" |
             word =="anc" |
             word =="south" |
             word =="africa" |
             word =="dstv403" |
             word =="t.co" |
             word =="itus" |
             word =="rt" |
             word =="amp"))

Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.

#calculate a frequency for each person and word
frequency <- tidy_covid_media_df %>% 
  group_by(screen_name) %>% 
  count(word, sort = TRUE) %>% 
  left_join(tidy_covid_media_df %>% 
              group_by(screen_name) %>% 
              summarise(total = n())) %>%
  mutate(freq = n/total)

Joining, by = "screen_name"

install.packages("ldatuning")


install.packages("devtools")
devtools::install_github("nikita-moor/ldatuning")


library("ldatuning")


library("topicmodels")
data("AssociatedPress", package="topicmodels")
dtm <- AssociatedPress[1:10, ]


result <- FindTopicsNumber(
  tidy_matrix,
  topics = seq(from = 2, to = 15, by = 1),
  metrics = c("Griffiths2004", "CaoJuan2009", "Arun2010", "Deveaud2014"),
  method = "Gibbs",
  control = list(seed = 77),
  mc.cores = 2L,
  verbose = TRUE
)


##  fit models... done.
##  calculate metrics:
##   Griffiths2004... done.
##   CaoJuan2009... done.
##   Arun2010... done.
##   Deveaud2014... done.


FindTopicsNumber_plot(result)